



'''simple docstring'''



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
							lowerCAmelCase_  :							Tuple             =    argparse.ArgumentParser(
							    description=(
							        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
							        ' Distillation'
							    )
							)
							parser.add_argument('--model_type', default='bert', choices=['bert'])
							parser.add_argument('--model_name', default='bert-base-uncased', type=str)
							parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
							parser.add_argument('--vocab_transform', action='store_true')
							lowerCAmelCase_  :							str             =    parser.parse_args()

							if args.model_type == "bert":
														lowerCAmelCase_  :							Optional[int]             =    BertForMaskedLM.from_pretrained(args.model_name)
														lowerCAmelCase_  :							Optional[int]             =    'bert'
							else:
														raise ValueError('args.model_type should be "bert".')

							lowerCAmelCase_  :							Union[str, Any]             =    model.state_dict()
							lowerCAmelCase_  :							Dict             =    {}

							for w in ["word_embeddings", "position_embeddings"]:
														lowerCAmelCase_  :							List[str]             =    state_dict[f'{prefix}.embeddings.{w}.weight']
							for w in ["weight", "bias"]:
														lowerCAmelCase_  :							Optional[Any]             =    state_dict[f'{prefix}.embeddings.LayerNorm.{w}']

							lowerCAmelCase_  :							List[Any]             =    0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														for w in ["weight", "bias"]:
																					lowerCAmelCase_  :							str             =    state_dict[
																					    f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
																					]
																					lowerCAmelCase_  :							List[Any]             =    state_dict[
																					    f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
																					]
																					lowerCAmelCase_  :							str             =    state_dict[
																					    f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
																					]

																					lowerCAmelCase_  :							Optional[int]             =    state_dict[
																					    f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
																					]
																					lowerCAmelCase_  :							Any             =    state_dict[
																					    f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
																					]

																					lowerCAmelCase_  :							Dict             =    state_dict[
																					    f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
																					]
																					lowerCAmelCase_  :							List[Any]             =    state_dict[
																					    f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
																					]
																					lowerCAmelCase_  :							Any             =    state_dict[
																					    f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
																					]
														std_idx += 1

							lowerCAmelCase_  :							Tuple             =    state_dict['cls.predictions.decoder.weight']
							lowerCAmelCase_  :							Union[str, Any]             =    state_dict['cls.predictions.bias']
							if args.vocab_transform:
														for w in ["weight", "bias"]:
																					lowerCAmelCase_  :							Optional[int]             =    state_dict[f'cls.predictions.transform.dense.{w}']
																					lowerCAmelCase_  :							str             =    state_dict[f'cls.predictions.transform.LayerNorm.{w}']

							print(f'N layers selected for distillation: {std_idx}')
							print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')

							print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
							torch.save(compressed_sd, args.dump_checkpoint)









'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					Optional[int]       ,		A	:					int					):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE       :   str         =      TaConfig.from_json_file(A					)
							print(F"""Building PyTorch model from configuration: {config}"""					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TaForConditionalGeneration(A					)

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(A       ,		A       ,		A					)

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Union[str, Any]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_  :							Tuple             =    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




